System and method of detecting mortgage related fraud

ABSTRACT

Embodiments include systems and methods of detecting fraud. In particular, one embodiment includes a system and method of detecting fraud in mortgage applications. For example, one embodiment includes a computerized method of detecting fraud that includes receiving mortgage data associated with an applicant and at least one entity related to processing of the mortgage data, determining a first score for the mortgage data based at least partly on a first model that is based on data from a plurality of historical mortgage transactions associated with the entity, and generating data indicative of fraud based at least partly on the first score. Other embodiments include systems and method of generating models for use in fraud detection systems.

RELATED APPLICATIONS

This application is a continuation of, and incorporates by reference in their entirety, U.S. patent application Ser. No. 11/526,208, filed Sep. 22, 2006 now U.S. Pat. No. 7,587,348, which claims the benefit of U.S. provisional patent application No. 60/785,902, filed Mar. 24, 2006 and U.S. provisional patent application No. 60/831,788, filed on Jul. 18, 2006.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to detecting fraud in financial transactions.

2. Description of the Related Technology

Fraud detection systems detect fraud in financial transactions. For example, a mortgage fraud detection system may be configured to analyze loan application data to identify applications that are being obtained using fraudulent application data.

However, existing fraud detection systems have failed to keep pace with the dynamic nature of financial transactions and mortgage application fraud. Moreover, such systems have failed to take advantage of the increased capabilities of computer systems. Thus, a need exists for improved systems and methods of detecting fraud.

SUMMARY OF CERTAIN INVENTIVE ASPECTS

The system, method, and devices of the invention each have several aspects, no single one of which is solely responsible for its desirable attributes. Without limiting the scope of this invention as expressed by the claims which follow, its more prominent features will now be discussed briefly. After considering this discussion, and particularly after reading the section entitled “Detailed Description of Certain Embodiments” one will understand how the features of this invention provide advantages that include improved fraud detection in financial transactions such as mortgage applications.

One embodiment includes a computerized method of detecting fraud. The method includes receiving mortgage data associated with an applicant and at least one entity related to processing of the mortgage data. The method further includes determining a first score for the mortgage data based at least partly on a first model that is based on data from a plurality of historical mortgage transactions associated with the at least one entity. The method further includes generating data indicative of fraud based at least partly on the first score.

Another embodiment includes a system for detecting fraud. The system includes a storage configured to receive mortgage data associated with an applicant and at least one entity related to processing of the mortgage application. The system further includes a processor configured to determine a first score for the mortgage data based at least partly on a first model that is based on data from a plurality of historical mortgage transactions associated with at least one entity. The system further includes generate data indicative of fraud based at least partly on the first score.

Another embodiment includes a system for detecting fraud. The system includes means for storing mortgage data associated with an applicant and at least one entity related to processing of the mortgage data, means for determining a first score for the mortgage data based at least partly on a first model that is based on data from a plurality of historical mortgage transactions associated with at least one entity, and means for generating data indicative of fraud based at least partly on the first score.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram illustrating a fraud detection system such as for use with a mortgage origination system.

FIG. 2 is a functional block diagram illustrating an example of the fraud detection system of FIG. 1 in more detail.

FIG. 3 is a functional block diagram illustrating an example of loan models in the fraud detection system of FIG. 2.

FIG. 4 is a functional block diagram illustrating examples of entity models in the fraud detection system of FIG. 2.

FIG. 5 is a flowchart illustrating model generation and use in the fraud detection system of FIG. 2.

FIG. 6 is a flowchart illustrating an example of using models in the fraud detection system of FIG. 2.

FIG. 7 is a flowchart illustrating an example of generating a loan model in the fraud detection system of FIG. 2.

FIG. 8 is a flowchart illustrating an example of generating entity models in the fraud detection system of FIG. 2.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

The following detailed description is directed to certain specific embodiments of the invention. However, the invention can be embodied in a multitude of different ways as defined and covered by the claims. In this description, reference is made to the drawings wherein like parts are designated with like numerals throughout.

Existing fraud detection systems may use transaction data in addition to data related to the transacting entities to identify fraud. Such systems may operate in either batch (processing transactions as a group of files at periodic times during the day) or real time mode (processing transactions one at a time, as they enter the system). However, the fraud detection capabilities of existing systems have not kept pace with either the types of fraudulent activity that have evolved or increasing processing and storage capabilities of computing systems.

For example, it has been found that, as discussed with reference to some embodiments, fraud detection can be improved by using stored past transaction data in place of, or in addition to, summarized forms of past transaction data. In addition, in one embodiment, fraud detection is improved by using statistical information that is stored according to groups of individuals that form clusters. In one such embodiment, fraud is identified with reference to deviation from identified clusters. In one embodiment, in addition to data associated with the mortgage applicant, embodiments of mortgage fraud detection systems may use data that is stored in association with one or more entities associated with the processing of the mortgage transaction such as brokers, appraisers, or other parties to mortgage transactions. The entities may be real persons or may refer to business associations, e.g., a particular appraiser, or an appraisal firm. Fraud generally refers to any material misrepresentation associated with a loan application and may include any misrepresentation which leads to a higher probability for the resulting loan to default or become un-sellable or require discount in the secondary market.

Mortgages may include residential, commercial, or industrial mortgages. In addition, mortgages may include first, second, home equity, or any other loan associated with a real property. In addition, it is to be recognized that other embodiments may also include fraud detection in other types of loans or financial transactions.

Exemplary applications of fraud detection relate to credit cards, debit cards, and mortgages. Furthermore, various patterns may be detected from external sources, such as data available from a credit bureau or other data aggregator.

FIG. 1 is a functional block diagram illustrating a fraud detection system 100 such as for use with a mortgage origination system 106. In other embodiments, the system 100 may be used to analyze applications for use in evaluating applications and/or funded loans by an investment bank or as part of due diligence of a loan portfolio. The fraud detection system 100 may receive and store data in a storage 104. The storage 104 may comprise one or more database servers and any suitable configuration of volatile and persistent memory. The fraud detection system 100 may be configured to receive mortgage application data from the mortgage origination system 106 and provide data indicative of fraud back to the mortgage origination system 106. In one embodiment, the fraud detection system 100 uses one or more models to generate the data indicative of fraud. In one embodiment, data indicative of fraud may also be provided to a risk manager system 108 for further processing and/or analysis by a human operator. The analysis system 108 may be provided in conjunction with the fraud detection system 100 or in conjunction with the mortgage origination system 106.

A model generator 110 may provide models to the fraud detection system 100. In one embodiment, the model generator 110 provides the models periodically to the system 100, such as when new versions of the system 100 are released to a production environment. In other embodiments, at least portion of the model generator 110 is included in the system 100 and configured to automatically update at least a portion of the models in the system 100.

FIG. 2 is a functional block diagram further illustrating an example of the fraud detection system 100. The system 100 may include an origination system interface 122 providing mortgage application data to a data preprocessing module 124. The origination system interface 122 receives data from the mortgage origination system 106 of FIG. 1. In other embodiments, the origination system interface 122 may be configured to receive data associated with funded mortgages and may be configured to interface with suitable systems other than, or in addition to, mortgage origination systems. For example, in one embodiment, the system interface 122 may be configured to receive “bid tapes” or other collections of data associated with funded mortgages for use in evaluating fraud associated with a portfolio of funded loans. In one embodiment the origination system interface 122 comprises a computer network that communicates with the origination system 106 to receive applications in real time or in batches. In one embodiment, the origination system interface 122 receives batches of applications via a data storage medium. The origination system interface 122 provides application data to the data preprocessing module 124 which formats application data into data formats used internally in the system 100. For example, the origination system interface 122 may also provide data from additional sources such as credit bureaus that may be in different formats for conversion by the data preprocessing module 124 into the internal data formats of the system 100. The origination system interface 122 and preprocessing module 124 also allow at least portions of a particular embodiment of the system 100 to be used to detect fraud in different types of credit applications and for different loan originators that have varying data and data formats. Table 1 lists examples of mortgage application data that may be used in various embodiments.

TABLE 1 Examples of Mortgage Data. Field Name Field Description Field Type portfolio_id Specifies which model was executed (TBD) char client_discretionary_field Reserved for client use char loan_no Unique Identifier for Loans char appl_date Application Date char appraisal_value Appraisal Value float borr_age Borrower Age long borr_last_name Borrower Last Name char borr_home_phone Borrower Home Phone char Internal Format: dddddddddd borr_ssn Borrower Social Security Number char Internal Format: ddddddddd coborr_last_name Co-Borrower Last Name char coborr_ssn Co-Borrower SSN char Internal Format: ddddddddd doc_type_code Numeric Code For Documentation Type (Stated, char Full, Partial, etc) Internal Mapping: 1: Full doc 3: Stated doc 4: Limited doc credit_score Credit Risk Score long loan_amount Loan Amount float prop_zipcode Five Digit Property Zip Code char Internal Format: ddddd status_desc Loan Status char borr_work_phone Borrower Business Phone Number char Internal Format: dddddddddd borr_self_employed Borrower Self Employed char Internal Mapping: Y: yes N: no borr_income Borrower Monthly Income float purpose_code Loan Purpose (Refi or Purchase) char Internal Mapping: 1: Purchase 1^(st) 4: Refinance 1^(st) 5: Purchase 2^(nd) 6: Refinance 2^(nd) borr_prof_yrs Borrower's Number of Years in this Profession float acct_mgr_name Account Manager name char ae_code Account Executive identifier (can be name or code) char category_desc Category Description char loan_to_value Loan to Value Ratio float combined_ltv Combined Loan to Value Ratio float status_date Status Date char Format MMDDYYYY borrower_employer Borrower Employer's Name char borrower_first_name Borrower first name char coborr_first_name Co-Borrower first name char Borr_marital_status Borrower marital status char mail_address Borrower mailing street address char mail_city Borrower mailing city char mail_state Borrower mailing state char mail_zipcode Borrower mailing address zipcode char prop_address Property street address char prop_city Property city char prop_state Property state char Back_end_ratio Back End Ratio float front_end_ratio Front End Ratio float Appraiser Data appr_code Unique identifier for appraiser char appr_first_name Appraiser first name char appr_last_name Appraiser last name char appr_tax_id Appraiser tax ID char appr_license_number Appraiser License Number char appr_license_expiredate Appraiser license expiration date char appr_license_state Appraiser license state code char company_name Appraiser company name char appr_cell_phone Appraiser cell phone char appr_work_phone Appraiser work phone char appr_fax Appraiser fax number char appr_address Appraiser current street address char appr_city Appraiser current city char appr_state Appraiser current state char appr_zipcode Appraiser current zip code char appr_status_code Appraiser status code (provide mapping) char appr_status_date Date of appraiser's current status char appr_email Appraiser e-mail address char Broker Data brk_code Broker Identifier char broker_first_name Broker first name (or loan officer first name) char broker_last_name Broker last name (or loan officer last name) char broker_tax_id Broker tax ID char broker_license_number Broker license number char broker_license_expiredate Broker license expiration date char broker_license_state Broker license state code char company_name Broker company name char brk_cell_phone Broker cell phone char brk_work_phone Broker work phone char brk_fax Broker fax number char brk_address Broker current street address char brk_city Broker current city char brk_state Broker current state char brk_zipcode Broker current zip code char brk_status_code Broker status code (provide mapping) char brk_status_date Date of broker's current status char brk_email Broker e-mail address char brk_fee_amount Broker fee amount long brk_point_amount Broker point amount long program_type_desc Program Type Description char loan_disposition Final disposition of loan during application process: char FUNDED - approved and funded NOTFUNDED - approved and not funded FRAUDDECLINE - confirmed fraud and declined CANCELLED - applicant withdrew application prior to any risk evaluation or credit decision PREVENTED - application conditioned for high risk/suspicion of misrepresentation and application was subsequently withdrawn or declined (suspected fraud but not confirmed fraud) DECLINED - application was declined for non- fraudulent reasons (e.g. credit risk) FUNDFRAUD - application was approved and funded and subsequently found to be fraudulent in post-funding QA process

The preprocessing module 124 may be configured to identify missing data values and provide data for those missing values to improve further processing. For example, the preprocessing module 124 may generate application data to fill missing data fields using one or more rules. Different rules may be used depending on the loan data supplier, on the particular data field, and/or on the distribution of data for a particular field. For example, for categorical fields, the most frequent value found in historical applications may be used. For numerical fields, the mean or median value of historical applications may be used. In addition, other values may be selected such as a value that is associated with the highest risk of fraud (e.g., assume the worst) or a value that is associated with the lowest risk of fraud (e.g., assume the best). In one embodiment, a sentinel value, e.g., a specific value that is indicative of a missing value to one or more fraud models may be used (allowing the fact that particular data is missing to be associated with fraud).

The preprocessing module 124 may also be configured to identify erroneous data or missing data. In one embodiment, the preprocessing module 124 extrapolates missing data based on data from similar applications, similar applicants, or using default data values. The preprocessing module 124 may perform data quality analysis such as one or more of critical error detection, anomaly detection, and data entry error detection. In one embodiment, applications failing one or more of these quality analyses may be logged to a data error log database 126.

In critical error detection, the preprocessing module 124 identifies applications that are missing data that the absence of which is likely to confound further processing. Such missing data may include, for example, appraisal value, borrower credit score, or loan amount. In one embodiment, no further processing is performed and a log or error entry is stored to the database 126 and/or provided to the loan origination system 106.

In anomaly detection, the preprocessing module 124 identifies continuous application data values that may be indicative of data entry error or of material misrepresentations. For example, high loan or appraisal amounts (e.g., above a threshold value) may be indicative of data entry error or fraud. Other anomalous data may include income or age data that is outside selected ranges. In one embodiment, such anomalous data is logged and the log provided to the origination system 106. In one embodiment, the fraud detection system 100 continues to process applications with anomalous data. The presence of anomalous data may be logged to the database 126 and/or included in a score output or report for the corresponding application.

In data entry detection, the preprocessing module 124 identifies non-continuous data such as categories or coded data that appear to have data entry errors. For example, telephone numbers or zip codes that have too many or too few digits, incomplete social security numbers, toll free numbers as home or work numbers, or other category data that fails to conform to input specifications may be logged. The presence of anomalous data may be logged to the database 126 and/or included in a score output or report for the corresponding application.

In one embodiment, the preprocessing module 124 queries an input history database 128 to determine if the application data is indicative of a duplicate application. A duplicate may indicate either resubmission of the same application fraudulently or erroneously. Duplicates may be logged. In one embodiment, no further processing of duplicates is performed. In other embodiments, processing of duplicates continues and may be noted in the final report or score. If no duplicate is found, the application data is stored to the input history database 124 to identify future duplicates.

The data preprocessing module 124 provides application data to one or more models for fraud scoring and processing. In one embodiment, application data is provided to one or more loan models 132 that generate data indicative of fraud based on application and applicant data. The data indicative of fraud generated by the loan models 132 may be provided to an integrator 136 that combines scores from one or more models into a final score. The data preprocessing module 124 may also provide application data to one or more entity models 140 that are configured to identify fraud based on data associated with entities involved in the processing of the application. Entity models may include models of data associated with loan brokers, loan officers or other entities involved in a loan application. More examples of such entity models 140 are illustrated with reference to FIG. 4. Each of the entity models may output data to an entity scoring module 150 that is configured to provide a score and/or one or more risk indicators associated with the application data. The term “risk indicator” refers to data values identified with respect to one or more data fields that may be indicative of fraud. The entity scoring module 150 may provide scores associated with one or more risk indicators associated with the particular entity or application. For example, appraisal value in combination with zip code may be a risk indicator associated with an appraiser model. In one embodiment, the entity scoring module 150 provides scores and indicators to the integrator 136 to generate a combined fraud score and/or set of risk indicators.

In one embodiment, the selection of risk indicators are based on criteria such as domain knowledge, and/or correlation coefficients between entity scores and fraud rate, if entity fraud rate is available. Correlation coefficient r_(i) between entity score s^(i) for risk indicator i and entity fraud rate f is defined as

$r_{i} = \frac{\sum\limits_{j = 1}^{N}{\left( {s_{j}^{i} - \overset{\_}{s}} \right)\left( {f_{j} - \overset{\_}{f}} \right)}}{\left( {N - 1} \right){{SD}\left( s^{i} \right)}{{SD}(f)}}$ where s_(j) ^(i) is the score for entity j on risk indicator i; and f_(j) is the fraud rate for entity j. If r_(i) is larger than a pre-defined threshold, then the risk indicator i is selected.

In one embodiment, the entity scoring model 150 combines each of the risk indicator scores for a particular entity using a weighted average or other suitable combining calculation to generate an overall entity score. In addition, the risk indicators having higher scores may also be identified and provided to the integrator 136.

In one embodiment, the combined score for a particular entity may be determined using one or more of the following models:

-   -   An equal weight average:

${s_{c} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}s^{i}}}},$ where N is the number of risk indicators;

-   -   A weighted average:

${s_{c} = {\sum\limits_{i = 1}^{N}{s^{i}\alpha^{i}}}},$ where N is the number of risk indicators and α^(i) is estimated based on how predictive risk indicator i is on individual loan level; a

-   -   A competitive committee:

${s_{c} = {\frac{1}{M}{\sum\limits_{i = 1}^{M}s^{i}}}},$ where s^(i)ε (set of largest M risk indicator scores).

If entity fraud rate or entity performance data (EPD) rate is available, the fraud/EPD rate may be incorporated with entity committee score to generate the combined entity score. The entity score S_(E) may be calculated using one of the following equations: S_(E)=S_(C), if relative entity fraud/EPD rate≦1; S _(E) =S _(D)+min(α*max(absoluteFraudRate,absoluteEPDRate),0.99)(998−S _(D)) if relative entity fraud/EPD rate>1 and S _(C) <S _(D;) S _(E) =S _(C)+min(α*max(absoluteFraudRate,absoluteEPDRate),0.99)(998−S _(C)) if relative entity fraud/EPD rate>1 and S _(C) ≧S _(D;) where α=b*tan h(α*(max(relativeFraudRate,relativeEPDRate)−1))

The preprocessing module 124 may also provide application data to a risky file processing module 156. In addition to application data, the risky file processing module 156 is configured to receive files from a risky files database 154. “Risky” files include portions of applications that are known to be fraudulent. It has been found that fraudulent applications are often resubmitted with only insubstantial changes in application data. The risky file processing module 156 compares each application to the risky files database 154 and flags applications that appear to be resubmissions of fraudulent applications. In one embodiment, risky file data is provided to the integrator 136 for integration into a combined fraud score or report.

The integrator 136 applies weights and/or processing rules to generate one or more scores and risk indicators based on the data indicative of fraud provided by one or more of the loan models 132, the entity models 140 and entity scoring modules 160, and the risky file processing module 156. In one embodiment, the risk indicator 136 generates a single score indicative of fraud along with one or more risk indicators relevant for the particular application. Additional scores may also be provided with reference to each of the risk indicators. The integrator 136 may provide this data to a scores and risk indicators module 160 that logs the scores to an output history database 160. In one embodiment, the scores and risk indicators module 160 identifies applications for further review by the risk manager 108 of FIG. 1. Scores may be real or integer values. In one embodiment, scores are numbers in the range of 1-999. In one embodiment, thresholds are applied to one or more categories to segment scores into high and low risk categories. In one embodiment, thresholds are applied to identify applications for review by the risk manager 108. In one embodiment, risk indicators are represented as codes that are indicative of certain data fields or certain values for data fields. Risk indicators may provide information on the types of fraud and recommended actions. For example, risk indicators might include a credit score inconsistent with income, high risk geographic area, etc. Risk indicators may also be indicative of entity historical transactions, e.g., a broker trend that is indicative of fraud.

A score review report module 162 may generate a report in one or more formats based on scores and risk indicators provided by the scores and risk indicators module 160. In one embodiment, the score review report module 162 identifies loan applications for review by the risk manager 108 of FIG. 1. One embodiment desirably improves the efficiency of the risk manager 108 by identifying applications with the highest fraud scores or with particular risk indicators for review thereby reducing the number of applications that need to be reviewed. A billing process 166 may be configured to generate billing information based on the results in the output history.

In one embodiment, the model generator 110 receives application data, entity data, and data on fraudulent and non-fraudulent applications and generates and updates models such as the entity models 140 either periodically or as new data is received.

FIG. 3 is a functional block diagram illustrating an example of the loan models 132 in the fraud detection system 100. In one embodiment, the loan models 132 may include one or more supervised models 170 and high risk rules models 172. Supervised models 170 are models that are generated based on training or data analysis that is based on historical transactions or applications that have been identified as fraudulent or non-fraudulent. Examples of implementations of supervised models 170 include scorecards, naïve Bayesian, decision trees, logistic regression, and neural networks. Particular embodiments may include one or more such supervised models 170.

The high risk rules models 172 may include expert systems, decision trees, and/or classification and regression tree (CART) models. The high risk rules models 172 may include rules or trees that identify particular data patterns that are indicative of fraud. In one embodiment, the high risk rules models 172 is used to generate scores and/or risk indicators.

In one embodiment, the rules, including selected data fields and condition parameters, are developed using the historical data used to develop the loan model 170. A set of high risk rule models 172 may be selected to include rules that have low firing rate and high hit rate. In one embodiment, when a rule i is fired, it outputs a score: S_(rule) ^(i). The score represents the fraud risk associated to the rule. The score may be a function of S _(rule) ^(i) =f(hitRateOfRule^(i),firingRateofRule^(i),scoreDistributionOfLoanAppModel), and S _(rule)=max(S _(rule) ¹ . . . S _(rule) ^(N)).

In one embodiment, the loan models 170 and 172 are updated when new versions of the system 100 are released into operation. In another embodiment, the supervised models 170 and the high risk rules models 172 are updated automatically. In addition, the supervised models 170 and the high risk rules models 172 may also be updated such as when new or modified data features or other model parameters are received.

FIG. 4 is a functional block diagram illustrating examples of the entity models 140 in the fraud detection system 100. It has been found that fraud detection performance can be increased by including models that operate on entities associated with a mortgage transaction that are in addition to the mortgage applicant. Scores for a number of different types of entities are calculated based on historical transaction data. The entity models may include one or more of an account executive model 142, a broker model 144, a loan officer model 146, and an appraiser (or appraisal) model 148. Embodiments may also include other entities associated with a transaction such as the lender. For example, in one embodiment, an unsupervised model, e.g., a clustering model such as k-means, is applied to risk indicators for historical transactions for each entity. A score for each risk indicator, for each entity, is calculated based on the relation of the particular entity to the clusters across the data set for the particular risk indicator.

By way of a simple example, for a risk indicator that is a single value, e.g., loan value for a broker, the difference between the loan value of each loan of the broker and the mean (assuming a simple Gaussian distribution of loan values) divided by the standard deviation of the loan values over the entire set of historical loans for all brokers might be used as a risk indicator for that risk indicator score. Embodiments that include more sophisticated clustering algorithms such as k-means may be used along with multi-dimensional risk indicators to provide for more powerful entity scores.

The corresponding entity scoring module 150 for each entity (e.g., account executive scoring module 152, broker scoring module 154, loan officer scoring module 156, and appraisal scoring module 158) may create a weighted average of the scores of a particular entity over a range of risk indicators that are relevant to a particular transaction.

FIG. 5 is a flowchart illustrating a method 300 of operation of the fraud detection system 100. The method 300 begins at a block 302 in which the supervised model is generated. In one embodiment, the supervised models 170 are generated based on training or data analysis that is based on historical transactions or applications that have been identified as fraudulent or non-fraudulent. Further details of generating supervised models are discussed with reference to FIG. 7. Moving to a block 304, the system 100 generates one or more unsupervised entity models such as the account executive model 142, the broker model 144, the loan officer model 146, or the appraiser (or appraisal) model 148. Further details of generating unsupervised models are discussed with reference to FIG. 8. Proceeding to a block 306, the system 100 applies application data to models such as supervised models 132 and entity models 150. The functions of block 306 may be repeated for each loan application that is to be processed. Further detail of applying data to the models is described with reference to FIG. 6.

In one embodiment, the model generator 110 generates and/or updates models as new data is received or at specified intervals such as nightly or weekly. In other embodiments, some models are updated continuously and others at specified intervals depending on factors such as system capacity, mortgage originator requirements or preferences, etc. In one embodiment, the entity models are updated periodically, e.g., nightly or weekly while the loan models are only updated when new versions of the system 100 are released into operation.

FIG. 6 is a flowchart illustrating an example of a method of performing the functions of the block 306 of FIG. 5 of using models in the fraud detection system 100 to process a loan application. The function 306 begins at a block 322 in which the origination system interface 122 receives loan application data. Next at a block 324, the data preprocessing module 124 preprocesses the application 324 as discussed above with reference to FIG. 2.

Moving to a block 326, the application data is applied to the supervised loan models 170 which provide a score indicative of the relative likelihood or probability of fraud to the integrator 136. In one embodiment, the supervised loan models 170 may also provide risk indicators. Next at a block 328, the high risk rules model 172 is applied to the application to generate one or more risk indicators, and/or additional scores indicative of fraud. Moving to a block 330, the application data is applied to one or more of the entity models 140 to generate additional scores and risk indicators associated with the corresponding entities of the models 140 associated with the transaction.

Next at a block 332, the integrator 136 calculates a weighted score and risk indicators based on scores and risk indicators from the supervised loan model 170, the high risk rules model 172, and scores of entity models 140. In one embodiment, the integrator 136 includes an additional model, e.g., a trained supervised model, that combines the various scores, weights, and risk factors provided by the models 170, 172, and 140.

Moving to a block 334, the scores and risk indicators module 160 and the score review report module 162 generate a report providing a weighted score along with one or more selected risk indicators. The selected risk indicators may include explanations of potential types of frauds and recommendations for action.

FIG. 7 is a flowchart illustrating an example of a method of performing the block 302 of FIG. 5 of generating the loan models 132 in the fraud detection system 100. Supervised learning algorithms identify a relationship between input features and target variables based on training data. In one embodiment, the target variables comprise the probability of fraud. Generally, the models used may depend on the size of the data and how complex a problem is. For example, if the fraudulent exemplars in historical data are less than about 5000 in number, smaller and simpler models may be used, so a robust model parameter estimation can be supported by the data size. The method 302 begins at a block 340 in which the model generator 110 receives historical mortgage data. The model generator 110 may extract and convert client historical data according to internal development data specifications, perform data analysis to determine data quality and availability, and rectify anomalies, such as missing data, invalid data, or possible data entry errors similar to that described above with reference to preprocessing module 124 of FIG. 2.

In addition, the model generator 110 may perform feature extraction including identifying predictive input variables for fraud detection models. The model generator 110 may use domain knowledge and mathematical equations applied to single or combined raw input data fields to identify predictive features. Raw data fields may be combined and transformed into discriminative features. Feature extraction may be performed based on the types of models for which the features are to be used. For example, linear models such as logistic regression and linear regression, work best when the relationships between input features and the target are linear. If the relationship is non-linear, proper transformation functions may be applied to convert such data to a linear function. In one embodiment, the model generator 110 selects features from a library of features for use in particular models. The selection of features may be determined by availability of data fields, and the usefulness of a feature for the particular data set and problem. Embodiments may use techniques such as filter and wrapper approaches, including information theory, stepwise regression, sensitivity analysis, data mining, or other data driven techniques for feature selection.

In one embodiment, the model generator 110 may segment the data into subsets to better model input data. For example, if subsets of a data set are identified with significantly distinct behavior, special models designed especially for these subsets normally outperform a general fit-all model. In one embodiment, a prior knowledge of data can be used to segment the data for generation of models. For example, in one embodiment, data is segregated geographically so that, for example, regional differences in home prices and lending practices do not confound fraud detection. In other embodiments, data driven techniques, e.g., unsupervised techniques such as clustering, are used to identify data segments that may benefit from a separate supervised model.

Proceeding to a block 342, the model generator 110 identifies a portion of the applications in the received application data (or segment of that data) that were fraudulent.

In one embodiment, the origination system interface 122 provides this labeling. Moving to a block 344, the model generator 110 identifies a portion of the applications that were non-fraudulent. Next at a block 346, the model generator 110 generates a model such as the supervised model 170 using a supervised learning algorithm to generate a model that distinguishes the fraudulent from the non-fraudulent transactions. In one embodiment, CART or other suitable model generation algorithms are applied to at least a portion of the data to generate the high risk rules models 172.

In one embodiment, historical data is split into multiple non-overlapped data sets. These multiple data sets are used for model generation and performance evaluation. For example, to train a neural network model, the data may be split into three sets, training set 1, training set 2, and validation. The training set 1 is used to train the neural network. The training set 2 is used during training to ensure the learning converge properly and to reduce overfitting to the training set 1. The validation set is used to evaluate the trained model performance. Supervised models may include one or more of scorecards, naïve Bayesian, decision trees, logistic regression, and neural networks.

FIG. 8 is a flowchart illustrating an example of a method of performing the block 304 of FIG. 5 of generating entity models 140 in the fraud detection system 100. The method 304 begins at a block 360 in which the model generator 110 receives historical mortgage applications. The model generator 110 may perform various processing functions such as described above with reference to the block 340 of FIG. 7. Next at a block 362, the model generator 110 receives data related to mortgage processing related entities such as an account executive, a broker, a loan officer, or an appraiser. Moving to a block 364, the model generator 110 selects risk indicators comprising one or more of the input data fields. In one embodiment, expert input is used to select the risk indicators for each type of entity to be modeled. In other embodiments, data driven techniques such as data mining are used to identify risk indicators.

Next at a block 368, the model generator 110 performs an unsupervised clustering algorithm such as k-means for each risk indicator for each type of entity. Moving to a block 370, the model generator 110 calculates scores for risk indicators for each received historical loan based on the data distance from data clusters identified by the clustering algorithm. For example, in a simple one cluster model where the data is distributed in a normal or Gaussian distribution, the distance may be a distance from the mean value. The distance/score may be adjusted based on the distribution of data for the risk indicator, e.g., based on the standard deviation in a simple normal distribution. Moving to a block 372, scores for each risk indicator and each entity are calculated based on model, such as a weighted average of each of the applications associated with each entity. Other embodiments may use other models.

It is to be recognized that depending on the embodiment, certain acts or events of any of the methods described herein can be performed in a different sequence, may be added, merged, or left out all together (e.g., not all described acts or events are necessary for the practice of the method). Moreover, in certain embodiments, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors, rather than sequentially.

Those of skill will recognize that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

While the above detailed description has shown, described, and pointed out novel features of the invention as applied to various embodiments, it will be understood that various omissions, substitutions, and changes in the form and details of the device or process illustrated may be made by those skilled in the art without departing from the spirit of the invention. As will be recognized, the present invention may be embodied within a form that does not provide all of the features and benefits set forth herein, as some features may be used or practiced separately from others. The scope of the invention is indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope. 

What is claimed is:
 1. A computerized method of detecting fraud, the method comprising: determining, by a computing system, based on mortgage data associated with an applicant, a first score for the mortgage data based at least partly on a first model that is based at least in part on historical data from a plurality of historical mortgage transactions that have each been identified as fraudulent or non-fraudulent, the first score indicative of a likelihood of the mortgage data being associated with a fraudulent transaction, the first model being generated based on one or more input data points within the historical data that are each correlated with a probability of fraud; generating, by computing system, at least one risk indicator for the mortgage data based at least partly on said first model, the risk indicator identifying a portion of the mortgage data that is indicative of a potential source of fraud; and generating a report comprising data indicative of whether the mortgage data is associated with a fraudulent transaction, said data comprising data reflective of at least the first score and the at least one risk indicator.
 2. The method of claim 1, wherein the mortgage data comprises at least one of mortgage application data, funded mortgage data, or bid tapes.
 3. The method of claim 1, wherein the plurality of historical mortgage transactions comprise a plurality of historical mortgage applications.
 4. The method of claim 1, further comprising determining, by the computing system, a second score for the mortgage data based at least partly on a second model that is based on data from a plurality of historical mortgage transactions associated with a plurality of entities related to processing of the transactions, wherein said report further comprises data reflective of the second score.
 5. The method of claim 4, wherein the plurality of entities include at least one of an account executive, a broker, a loan officer, or an appraiser.
 6. The method of claim 1, wherein the first model is a computer data model that comprises at least one of neural network, logistic regression, linear regression, decision trees, a classification and regression tree (CART) model, and an expert system.
 7. The method of claim 6, wherein said first model is retrieved by the computing system from an electronic database.
 8. The method of claim 1, wherein generating said at least one risk indicator of fraud further comprises: identifying within the mortgage data, presence of at least one of said one or more input data points that is each correlated with a probability of fraud in said first model.
 9. The method of claim 1, wherein the first model includes at least one cluster of at least a portion of the data of the plurality of historical mortgage transactions.
 10. The method of claim 9, wherein determining the first score comprises comparing data of the mortgage data with the at least one cluster.
 11. The method of claim 1, wherein the mortgage data comprises at least one of: data indicative of at least one person associated with a mortgage application, data indicative of income of a mortgage applicant, and data indicative of a property subject to a mortgage transaction.
 12. The method of claim 1, further comprising selecting an application associated with the mortgage data for further review based on the data indicative of whether the mortgage data is associated with a fraudulent transaction, where said selecting can be performed manually or by the computing system.
 13. The method of claim 1, wherein generating the at least one risk indicator is performed substantially in real time.
 14. The method of claim 1, wherein generating the at least one risk indicator is performed in a batch mode.
 15. The method of claim 1 further comprising: determining, by the computing system, a second score for the mortgage data based at least partly on one or more risk rule models comprising rules that identify data patterns indicative of fraud; and generating, by the computing system, a weighted score based at least in part on the first and second scores, wherein said data indicative of whether the mortgage data is associated with a fraudulent transaction comprises data indicative of at least the weighted score.
 16. The method of claim 1, further comprising: generating, by the computing system, said first model by using at least one of a neural network, logistic regression, linear regression, decision trees, a classification and regression tree (CART) model, and an expert system.
 17. The method of claim 1, further comprising: retrieving, by the computing system, said mortgage data from an electronic database.
 18. The method of claim 1, further comprising: retrieving, by the computing system, said historical data from an electronic database.
 19. The method of claim 1, further comprising repeating said determining and generating for data associated with a statistically significant number of mortgage applicants.
 20. A system for detecting fraud, the system comprising: a storage system configured to store mortgage data associated with an applicant; and a computing system, comprising one or more computing devices, configured to implement: a score integrator component configured to: determine a first score for the mortgage data based at least partly on a first model that is based at least in part on historical data from a plurality of historical mortgage transactions that have each been identified as fraudulent or non-fraudulent, the first score indicative of a likelihood of the mortgage data being associated with a fraudulent transaction, the first model being generated based on one or more input data points within the historical data that are each correlated with a probability of fraud; and generate at least one risk indicator for the mortgage data based at least partly on said first model, the risk indicator identifying a portion of the mortgage data that is indicative of a potential source of fraud; and a report component configured to generate a report comprising data indicative of whether the mortgage data is associated with a fraudulent transaction, said data comprising data reflective of at least the first score and the at least one risk indicator.
 21. The system of claim 20, wherein said score integrator component is further configured to determine a second score for the mortgage data based at least partly on a second model that is based on data from a plurality of historical mortgage transactions associated with a plurality of entities related to processing of the transactions, wherein said report comprises data reflective of the second score.
 22. The system of claim 21, wherein the score integrator component is further configured to generate at least one risk indicator of fraud based on said second model.
 23. The system of claim 20, wherein the first model includes at least one cluster of at least a portion of the data of the plurality of historical mortgage transactions and wherein the score integrator component determines the first score at least in part by comparing data of the mortgage data with the at least one cluster.
 24. The system of claim 20, wherein the computing system is further configured to select an application associated with the mortgage data for further review based at least partly on the data indicative of whether the mortgage data is associated with a fraudulent transaction.
 25. The system of claim 20, wherein the computing system is further configured to implement: a model generator component to: generate said first model based at least in part on said historical data; and generate a second model based on data from a plurality of historical mortgage transactions associated with at least one entity related to processing of the mortgage transactions; wherein the computing system is further configured to: store the first model in a first computer readable medium; and store the second model in a second computer readable medium.
 26. The system of claim 20, wherein the first model is a computer data model generated by a model generator component implemented in the computing system.
 27. The system of claim 26, wherein the first model is generated based on at least one of a neural network, logistic regression, linear regression, decision trees, a classification and regression tree (CART) model, and an expert system.
 28. The system of claim 20, wherein the storage system comprises one or more electronic databases. 